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Ming-Feng Chang

Researcher at National Chiao Tung University

Publications -  34
Citations -  384

Ming-Feng Chang is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Voice over IP & Cellular network. The author has an hindex of 7, co-authored 31 publications receiving 341 citations. Previous affiliations of Ming-Feng Chang include National Chiayi University & University of Illinois at Urbana–Champaign.

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A direction-based location update scheme with a line-paging strategy for PCS networks

TL;DR: A direction-based location update (DBLU) scheme using a line-paging strategy to reduce the paging cost and a moving direction identification mechanism using only simple computations detects the change of moving direction and updates the mobile's location.
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One-pass GPRS and IMS authentication procedure for UMTS

TL;DR: An one-pass authentication procedure that only needs to perform GPRS authentication for IMS users is proposed that may save up to 50% of the IMS registration/authentication traffic, as compared with the 3GPP two-pass procedure.
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Diagnosis and repair of memory with coupling faults

TL;DR: The authors examine both diagnosis and repair of coupling faults in RAMs utilizing spare rows and columns and shows that a coupling fault is repaired if its coupling cell is replaced by utilizing a spare row or its coupled cell is replacement by utilize a spareRow or column.
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Optimal diagnosis procedures for k-out-of-n structures

TL;DR: Diagnosis strategies are investigated for repairable VLSI and WSI structures based on integrated diagnosis and repair and a compact representation of the optimal diagnosis procedure is described.
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Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0

TL;DR: A novel method to forecast travel time based on big data collected from the industrial IoT infrastructure that separates the global regression tree model based on the gradient boosting decision tree into several partitions to capture the time-varying features simultaneously.